Connecting the Dots – Visualizing Paths in Tableau

I had been planning to write this post for a long time. Not only have I been asked many times how to do this in my daily consulting work, but especially during and after my hands-on training “Stretching the Boundaries with Advanced Mapping” at our Tableau Conference On Tour 2017 in Berlin earlier this year. The question is pretty simple: How can I draw paths in Tableau? Oftentimes these are some kind of movement data, e.g. refugees or flight connections. The way to do this in Tableau is actually very easy – and some of the recently introduced features made it even easier – but it’s imperative to understand how Tableau draws lines and how the data therefore needs to be structured.

The OpenFlights.org route network visualized in Tableau

The OpenFlights.org route network visualized in Tableau

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Population Lines - the Tableau Edition

Population Lines – the Tableau Edition

In 2013 Dr. James Cheshire from the Centre for Advanced Spatial Analysis at the University College London created a data visualization that was critically acclaimed back then and saw something of a renaissance a few weeks ago when a modified version by Henrik Lindberg made its way onto the Reddit front page. I had been mesmerized by the viz from the beginning, so when it reappeared in my blog reader I decided I had to try reproducing it in Tableau.

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The last available report per BookID is marked

Reporting Self-Updating Data Using Wildcard Union in Tableau

Imagine you have some kind of system that produces reports on your data – for this example I randomly decided to use bookings for events -, and these reports are published on a regular schedule. Now you want to see two things in your report:

  1. The current status of participants per event – both for past events (i.e. the actual number of participants) and for future events (i.e. the current number of people registered).
  2. An overview of how the number of people registered changed over time.

Also, your source system is publishing these data as .csv files. How can this be done?

Well, very easily using the new wildcard union feature introduced in Tableau Desktop 10.1! Read on to see how this can be done.

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How to Analyse and Make Sense of Humongous Datasets

This was the title of an invited talk I gave at MongoDB’s first public event in Germany on September 26th. MongoDB is awesome in that it is able to handle large amounts of both structured (read: relational sources) and unstructured (read: NoSQL) data. Also, the ability to integrate data from a number of disparate sources and the fast response times make it a good fit to be used together with Tableau for any kind of ad-hoc analysis task. In order to show these capabilities and also to have some fun I decided to spice up the introduction of Tableau I provided there with a little live demo of how this looks in real life. When it came to select what data to use I decided to go with movie data – a logical choice since we have the Tableau Cinema Tour coming up soon (see below). Also, one of our founding fathers here at Tableau is Prof. Pat Hanrahan, who received his first Academy Award (of three!) for the development of the RenderMan┬« Software that only made movies like Toy Story possible in the first place. Continue reading →